Navigating Challenges and Technical Debt in LLMs Deployment

Explore the complex landscape of deploying Large Language Models in production environments through this 16-minute conference talk that addresses critical challenges and technical debt issues. Learn about the ethical concerns surrounding LLM deployment, including bias amplification, misinformation risks, privacy vulnerabilities, and societal impacts such as employment displacement. Discover the sophisticated engineering solutions required for LLM customization that go beyond standard machine learning libraries and inference engines. Gain insights into the operational complexities of ML pipeline deployment and understand why careful management and ethical considerations are essential when implementing LLMs at scale. The presentation draws from real-world experience in AI engineering leadership and co-authored research on LLM deployment challenges, providing practical perspectives on navigating the technical debt that accumulates in large-scale AI systems.